{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1b575c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[5.1, 3.5, 1.4, 0.2],\n",
       "        [4.9, 3. , 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.3, 0.2],\n",
       "        [4.6, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.6, 1.4, 0.2],\n",
       "        [5.4, 3.9, 1.7, 0.4],\n",
       "        [4.6, 3.4, 1.4, 0.3],\n",
       "        [5. , 3.4, 1.5, 0.2],\n",
       "        [4.4, 2.9, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5.4, 3.7, 1.5, 0.2],\n",
       "        [4.8, 3.4, 1.6, 0.2],\n",
       "        [4.8, 3. , 1.4, 0.1],\n",
       "        [4.3, 3. , 1.1, 0.1],\n",
       "        [5.8, 4. , 1.2, 0.2],\n",
       "        [5.7, 4.4, 1.5, 0.4],\n",
       "        [5.4, 3.9, 1.3, 0.4],\n",
       "        [5.1, 3.5, 1.4, 0.3],\n",
       "        [5.7, 3.8, 1.7, 0.3],\n",
       "        [5.1, 3.8, 1.5, 0.3],\n",
       "        [5.4, 3.4, 1.7, 0.2],\n",
       "        [5.1, 3.7, 1.5, 0.4],\n",
       "        [4.6, 3.6, 1. , 0.2],\n",
       "        [5.1, 3.3, 1.7, 0.5],\n",
       "        [4.8, 3.4, 1.9, 0.2],\n",
       "        [5. , 3. , 1.6, 0.2],\n",
       "        [5. , 3.4, 1.6, 0.4],\n",
       "        [5.2, 3.5, 1.5, 0.2],\n",
       "        [5.2, 3.4, 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.6, 0.2],\n",
       "        [4.8, 3.1, 1.6, 0.2],\n",
       "        [5.4, 3.4, 1.5, 0.4],\n",
       "        [5.2, 4.1, 1.5, 0.1],\n",
       "        [5.5, 4.2, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.6, 1.4, 0.1],\n",
       "        [4.4, 3. , 1.3, 0.2],\n",
       "        [5.1, 3.4, 1.5, 0.2],\n",
       "        [5. , 3.5, 1.3, 0.3],\n",
       "        [4.5, 2.3, 1.3, 0.3],\n",
       "        [4.4, 3.2, 1.3, 0.2],\n",
       "        [5. , 3.5, 1.6, 0.6],\n",
       "        [5.1, 3.8, 1.9, 0.4],\n",
       "        [4.8, 3. , 1.4, 0.3],\n",
       "        [5.1, 3.8, 1.6, 0.2],\n",
       "        [4.6, 3.2, 1.4, 0.2],\n",
       "        [5.3, 3.7, 1.5, 0.2],\n",
       "        [5. , 3.3, 1.4, 0.2],\n",
       "        [7. , 3.2, 4.7, 1.4],\n",
       "        [6.4, 3.2, 4.5, 1.5],\n",
       "        [6.9, 3.1, 4.9, 1.5],\n",
       "        [5.5, 2.3, 4. , 1.3],\n",
       "        [6.5, 2.8, 4.6, 1.5],\n",
       "        [5.7, 2.8, 4.5, 1.3],\n",
       "        [6.3, 3.3, 4.7, 1.6],\n",
       "        [4.9, 2.4, 3.3, 1. ],\n",
       "        [6.6, 2.9, 4.6, 1.3],\n",
       "        [5.2, 2.7, 3.9, 1.4],\n",
       "        [5. , 2. , 3.5, 1. ],\n",
       "        [5.9, 3. , 4.2, 1.5],\n",
       "        [6. , 2.2, 4. , 1. ],\n",
       "        [6.1, 2.9, 4.7, 1.4],\n",
       "        [5.6, 2.9, 3.6, 1.3],\n",
       "        [6.7, 3.1, 4.4, 1.4],\n",
       "        [5.6, 3. , 4.5, 1.5],\n",
       "        [5.8, 2.7, 4.1, 1. ],\n",
       "        [6.2, 2.2, 4.5, 1.5],\n",
       "        [5.6, 2.5, 3.9, 1.1],\n",
       "        [5.9, 3.2, 4.8, 1.8],\n",
       "        [6.1, 2.8, 4. , 1.3],\n",
       "        [6.3, 2.5, 4.9, 1.5],\n",
       "        [6.1, 2.8, 4.7, 1.2],\n",
       "        [6.4, 2.9, 4.3, 1.3],\n",
       "        [6.6, 3. , 4.4, 1.4],\n",
       "        [6.8, 2.8, 4.8, 1.4],\n",
       "        [6.7, 3. , 5. , 1.7],\n",
       "        [6. , 2.9, 4.5, 1.5],\n",
       "        [5.7, 2.6, 3.5, 1. ],\n",
       "        [5.5, 2.4, 3.8, 1.1],\n",
       "        [5.5, 2.4, 3.7, 1. ],\n",
       "        [5.8, 2.7, 3.9, 1.2],\n",
       "        [6. , 2.7, 5.1, 1.6],\n",
       "        [5.4, 3. , 4.5, 1.5],\n",
       "        [6. , 3.4, 4.5, 1.6],\n",
       "        [6.7, 3.1, 4.7, 1.5],\n",
       "        [6.3, 2.3, 4.4, 1.3],\n",
       "        [5.6, 3. , 4.1, 1.3],\n",
       "        [5.5, 2.5, 4. , 1.3],\n",
       "        [5.5, 2.6, 4.4, 1.2],\n",
       "        [6.1, 3. , 4.6, 1.4],\n",
       "        [5.8, 2.6, 4. , 1.2],\n",
       "        [5. , 2.3, 3.3, 1. ],\n",
       "        [5.6, 2.7, 4.2, 1.3],\n",
       "        [5.7, 3. , 4.2, 1.2],\n",
       "        [5.7, 2.9, 4.2, 1.3],\n",
       "        [6.2, 2.9, 4.3, 1.3],\n",
       "        [5.1, 2.5, 3. , 1.1],\n",
       "        [5.7, 2.8, 4.1, 1.3],\n",
       "        [6.3, 3.3, 6. , 2.5],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [7.1, 3. , 5.9, 2.1],\n",
       "        [6.3, 2.9, 5.6, 1.8],\n",
       "        [6.5, 3. , 5.8, 2.2],\n",
       "        [7.6, 3. , 6.6, 2.1],\n",
       "        [4.9, 2.5, 4.5, 1.7],\n",
       "        [7.3, 2.9, 6.3, 1.8],\n",
       "        [6.7, 2.5, 5.8, 1.8],\n",
       "        [7.2, 3.6, 6.1, 2.5],\n",
       "        [6.5, 3.2, 5.1, 2. ],\n",
       "        [6.4, 2.7, 5.3, 1.9],\n",
       "        [6.8, 3. , 5.5, 2.1],\n",
       "        [5.7, 2.5, 5. , 2. ],\n",
       "        [5.8, 2.8, 5.1, 2.4],\n",
       "        [6.4, 3.2, 5.3, 2.3],\n",
       "        [6.5, 3. , 5.5, 1.8],\n",
       "        [7.7, 3.8, 6.7, 2.2],\n",
       "        [7.7, 2.6, 6.9, 2.3],\n",
       "        [6. , 2.2, 5. , 1.5],\n",
       "        [6.9, 3.2, 5.7, 2.3],\n",
       "        [5.6, 2.8, 4.9, 2. ],\n",
       "        [7.7, 2.8, 6.7, 2. ],\n",
       "        [6.3, 2.7, 4.9, 1.8],\n",
       "        [6.7, 3.3, 5.7, 2.1],\n",
       "        [7.2, 3.2, 6. , 1.8],\n",
       "        [6.2, 2.8, 4.8, 1.8],\n",
       "        [6.1, 3. , 4.9, 1.8],\n",
       "        [6.4, 2.8, 5.6, 2.1],\n",
       "        [7.2, 3. , 5.8, 1.6],\n",
       "        [7.4, 2.8, 6.1, 1.9],\n",
       "        [7.9, 3.8, 6.4, 2. ],\n",
       "        [6.4, 2.8, 5.6, 2.2],\n",
       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression # 逻辑回归 // 常用于机器学习当中分类问题\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 加载数据集\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f525df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据转换为DataFrame\n",
    "df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "df['target'] = iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2f98e34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制直方图\n",
    "df.hist(bins=40, figsize=(10, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7166f04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      " 4   target             150 non-null    int32  \n",
      "dtypes: float64(4), int32(1)\n",
      "memory usage: 5.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a6de9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)      target  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.199333    1.000000  \n",
       "std            0.762238    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f80afb06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# 热力图可视化\n",
    "plt.figure(figsize=(12, 10)) # 设置图片大小\n",
    "sns.heatmap(df[::10], annot=True, fmt=\".2f\", cmap=\"coolwarm\") # 说明：annot=True表示在每个单元格中显示数值，fmt=\".2f\"表示保留两位小数，cmap=\"coolwarm\"表示颜色映射\n",
    "plt.title(\"Correlation Matrix\") # 标题\n",
    "plt.show() # 显示图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d5d14849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length  sepal width  petal length  petal width  label\n",
      "0             5.1          3.5           1.4          0.2      0\n",
      "1             4.9          3.0           1.4          0.2      0\n",
      "2             4.7          3.2           1.3          0.2      0\n",
      "3             4.6          3.1           1.5          0.2      0\n",
      "4             5.0          3.6           1.4          0.2      0\n",
      "..            ...          ...           ...          ...    ...\n",
      "145           6.7          3.0           5.2          2.3      2\n",
      "146           6.3          2.5           5.0          1.9      2\n",
      "147           6.5          3.0           5.2          2.0      2\n",
      "148           6.2          3.4           5.4          2.3      2\n",
      "149           5.9          3.0           5.1          1.8      2\n",
      "\n",
      "[150 rows x 5 columns]\n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [5.4 3.4 1.7 0.2]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [5.  3.  1.6 0.2]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [5.5 4.2 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.2]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.9 3.6 1.4 0.1]\n",
      " [4.4 3.  1.3 0.2]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.  3.5 1.3 0.3]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [7.  3.2 4.7 1.4]\n",
      " [6.4 3.2 4.5 1.5]\n",
      " [6.9 3.1 4.9 1.5]\n",
      " [5.5 2.3 4.  1.3]\n",
      " [6.5 2.8 4.6 1.5]\n",
      " [5.7 2.8 4.5 1.3]\n",
      " [6.3 3.3 4.7 1.6]\n",
      " [4.9 2.4 3.3 1. ]\n",
      " [6.6 2.9 4.6 1.3]\n",
      " [5.2 2.7 3.9 1.4]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.9 3.  4.2 1.5]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.1 2.9 4.7 1.4]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [5.6 3.  4.5 1.5]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.9 3.2 4.8 1.8]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [6.1 2.8 4.7 1.2]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [6.6 3.  4.4 1.4]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [6.7 3.  5.  1.7]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [6.3 2.3 4.4 1.3]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.8 2.6 4.  1.2]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [6.3 3.3 6.  2.5]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [7.1 3.  5.9 2.1]\n",
      " [6.3 2.9 5.6 1.8]\n",
      " [6.5 3.  5.8 2.2]\n",
      " [7.6 3.  6.6 2.1]\n",
      " [4.9 2.5 4.5 1.7]\n",
      " [7.3 2.9 6.3 1.8]\n",
      " [6.7 2.5 5.8 1.8]\n",
      " [7.2 3.6 6.1 2.5]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [6.8 3.  5.5 2.1]\n",
      " [5.7 2.5 5.  2. ]\n",
      " [5.8 2.8 5.1 2.4]\n",
      " [6.4 3.2 5.3 2.3]\n",
      " [6.5 3.  5.5 1.8]\n",
      " [7.7 3.8 6.7 2.2]\n",
      " [7.7 2.6 6.9 2.3]\n",
      " [6.  2.2 5.  1.5]\n",
      " [6.9 3.2 5.7 2.3]\n",
      " [5.6 2.8 4.9 2. ]\n",
      " [7.7 2.8 6.7 2. ]\n",
      " [6.3 2.7 4.9 1.8]\n",
      " [6.7 3.3 5.7 2.1]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [6.1 2.6 5.6 1.4]\n",
      " [7.7 3.  6.1 2.3]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [6.  3.  4.8 1.8]\n",
      " [6.9 3.1 5.4 2.1]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [5.9 3.  5.1 1.8]]\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "准确率：1.00\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "iris = load_iris()\n",
    "df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "df['label'] = iris.target\n",
    "df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']\n",
    "\n",
    "print(df)\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "print(X)\n",
    "print(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 划分数据集（80%训练集，20%测试集） // 随机种子42\n",
    "\n",
    "lr = LogisticRegression(\n",
    "    verbose=True,\n",
    "    max_iter=1000,\n",
    ")\n",
    "\n",
    "# 创建一个逻辑回归对象\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_test) # 预测测试集\n",
    "\n",
    "print(f'准确率：{accuracy_score(y_test, y_pred):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b74c2e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [100/1000], Loss: 0.5980, Accuracy: 0.7667\n",
      "Epoch [200/1000], Loss: 0.4703, Accuracy: 0.9000\n",
      "Epoch [300/1000], Loss: 0.3995, Accuracy: 0.9533\n",
      "Epoch [400/1000], Loss: 0.3469, Accuracy: 0.9667\n",
      "Epoch [500/1000], Loss: 0.3040, Accuracy: 0.9733\n",
      "Epoch [600/1000], Loss: 0.2684, Accuracy: 0.9733\n",
      "Epoch [700/1000], Loss: 0.2392, Accuracy: 0.9733\n",
      "Epoch [800/1000], Loss: 0.2152, Accuracy: 0.9733\n",
      "Epoch [900/1000], Loss: 0.1957, Accuracy: 0.9733\n",
      "Epoch [1000/1000], Loss: 0.1797, Accuracy: 0.9733\n",
      "预测结果: 0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# 加载数据\n",
    "iris = load_iris()\n",
    "df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
    "df['label'] = iris.target\n",
    "df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']\n",
    "\n",
    "# 转成张量\n",
    "data = torch.from_numpy(df.values).float()\n",
    "x_data = data[:, :-1]\n",
    "y_data = data[:, -1].long()  # 关键：CrossEntropyLoss 需要 LongTensor\n",
    "\n",
    "# 可视化 (选两维度画散点)\n",
    "plt.scatter(x_data[y_data == 0, 2], x_data[y_data == 0, 3], label='label=0')\n",
    "plt.scatter(x_data[y_data == 1, 2], x_data[y_data == 1, 3], label='label=1')\n",
    "plt.scatter(x_data[y_data == 2, 2], x_data[y_data == 2, 3], label='label=2')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('petal width')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 定义网络\n",
    "class Net(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.hidden = torch.nn.Linear(4, 10)\n",
    "        self.out = torch.nn.Linear(10, 3)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.hidden(x))\n",
    "        x = self.out(x)\n",
    "        return x\n",
    "\n",
    "net = Net()\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.01)\n",
    "loss_func = torch.nn.CrossEntropyLoss()\n",
    "epochs = 1000\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    out = net(x_data)\n",
    "    loss = loss_func(out, y_data)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if (epoch + 1) % 100 == 0:\n",
    "        _, pred = torch.max(out, 1)\n",
    "        acc = (pred == y_data).sum().item() / y_data.size(0)\n",
    "        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}, Accuracy: {acc:.4f}')\n",
    "\n",
    "\n",
    "# 预测\n",
    "test_data = torch.tensor([[5.1, 3.5, 1.4, 0.2]])\n",
    "pred = net(test_data)\n",
    "_, pred = torch.max(pred, 1)\n",
    "print(f'预测结果: {pred.item()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c2cb18a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
